Brand accuracy audit
AI models now answer questions about your company every day. I check whether what they say is actually correct, then help you fix what isn't.
When AI states the wrong facts about your brand, at scale
Factual accuracy check
I run an expanded set of prompts across the main AI models, because the same question returns different answers to different users. Widening the prompts separates one-off responses from the claims that come up again and again, so you get reliable directional data on what AI repeats about you. Each statement about your products, pricing, founding, leadership and public claims is checked against your real source of truth and marked accurate, outdated or wrong.
Hallucination and outdated-data detection
This is where I catch the invented and the stale: features the model imagines, offers that ended months ago, old leadership names, and the competitor mix-ups where your details get attached to another company. AI hallucination is one cause, but plenty of the errors are simply old facts the model never updated, and both count as brand misinformation worth correcting.
Source-of-truth mapping
Wrong answers come from somewhere. I trace where the bad information lives, whether that is an outdated Wikipedia line, a stale directory listing, an old press piece or your own pages, and I look at what the models tend to ground on and cite when they answer. Knowing the source is what makes a correction realistic rather than guesswork.
Correction and remediation roadmap
You get a prioritised plan, ordered by how much each fix moves the needle on AI factual accuracy. It covers your own content and structured data, plus the third-party sources the models lean on most. I focus on the changes that influence the record AI reads from, so corrections have a real chance of sticking.
Brand governance audit FAQs
What's the difference between a sentiment audit and a governance accuracy audit?
Sentiment looks at tone, whether AI describes your brand in a positive, neutral or negative light. Governance accuracy looks at facts, whether the claims are simply true or false. The two pair well together, and I often run them side by side.
How can I influence what AI says if I don't control the model?
You don't edit the model, but you do shape what it reads. Models ground their answers on public sources and your own site, so correcting those sources, adding clear structured data and tidying third-party listings all change the input. Over time that moves the output.
How do you find where AI got the wrong information?
I look at the sources the models cite, then work backwards from each wrong claim to the pages and listings most likely to have caused it. Some errors trace to one bad source, others to a pattern repeated across several. Mapping that is part of every audit.
How quickly do corrections show up in AI answers?
It varies. Some changes appear within weeks as models and their sources refresh, while others take longer because older information lingers in training data and cached pages. I set honest expectations up front and suggest what to re-check over time.
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